Classification and preprocessing of data for television recommendation system

This paper presents a general approach for personalized recommendation system for next generation of smart television. Hence the TV provides a hybrid broadband and broadcast transmission both can collect information. Aim is to combine television with internet content in order to provide interactive and personalized recommendation. This improves user´s watching experience. Since these two sources have a different format, data integration is needed. Additionally, the data have to be preprocessed in order to remove so-called “global effects” and improve further classification. Classifier is based on k-nearest neighbors (kNN) improved approach, designed for the Neflix price. It was described for an on-demand-video, where no time consideration is needed. On the other hand, we include time as a factor worth considering as it is typical for TV program schedule. Final recommendation includes also Fuzzy logic based training sequence selection and final weight correction.